A communication network accessed and used by multiple users or customers may include a huge number of communication and computing devices (e.g. computers, routers, switches, etc. . . . ) also referred to as network elements. Each device or network element may support different operations and may follow different policies. At any given moment, large numbers of users may attempt to access the network and may cause vast amounts of communication traffic to traverse the network. In some cases, the amount of data traffic attempting to pass through a network element may exceed the maximum capacity of that network element, and a condition known as a bottleneck may result.
In a network operated by a network operator for access by a group of customers, each customer may have different and complex needs, which needs may be stated in a contract (e.g. Service Level Agreement (“SLA”)) with the network operator. An SLA between a customer and a network operator may contain provisions guaranteeing minimum Quality of Service (“QOS”) for the given customer and for one or more given applications of the specific customer. QOS is defined, in part, by the ability of a network to carry data traffic that complies with cervix minimal resources and service requirements (e.g., bandwidth, delay, jitter, etc.). A user's QOS may be guaranteed within their SLA with the network operator, and certain SLAs may impose penalties on a network operator if a customer's QOS fans below a threshold level.
Many techniques and methodologies are known for establishing and maintaining QOS levels across a network and within specific network elements or devices. Methods including Weighted Fair Queuing (WFQ), Differentiated Services (Diffserv), Multiprotocol Label Switching (MPLS), Resource Reservation Protocol (RSVP), and others are used to define network policies which attempt to avoid network congestion or bottlenecks. However, when a communication network experiences a surge in traffics or a reduction in the network capacities (e.g., caused by faults), fixed network policies may not be able to compensate for this surge or reduction of capacities, and certain network elements may become congested. Operating at or above capacity along certain data paths, the network may experience bottlenecks and an overall QOS degradation for one or a group of its users.
One method of preventing a user's QOS from falling below a predefined level due to congestion caused by data traffic of a new user is to limit or deny access to the network to new users or new applications. This method involving denial of service requires either that s a new user or new application be denied a request for access, or that a session of a user or application currently using the network be terminated. In this manner, the total number of users or applications using the network may be kept to a number sufficiently low such that the QOS of the majority of existing communication sessions is not degraded. However, refusal of service may translate into lost revenues and in other instances may mean the loss of highly valued customers.
In order to avoid the above-mentioned conditions and commercial results, extensive work has been done to optimize the throughput and QOS compliance of communication networks. Traditionally, however, optimization has emphasized physical network design, selection and topology of network elements (e.g. which components are needed and how to connect them) and routing architecture. Some methods of the prior art use a combination of admission control and dynamic routing to optimize a network.
Some methods of the prior art have attempted to optimize a network with respect to revenues or profits related to the operation of the network. These methods, however, all use simplistic revenue models which do not take into consideration factors such as customer usage over time, customer payment patterns, customer value to the operator, etc. Network optimization methods of the prior art are thus lacking in many respects.